Corentin Dancette

PhD student at Sorbone Université. Paris, France

Hi! I am a french computer science student, I gratuated from Ecole Centrale Paris, France, and Georgia Institute of Technology, USA.

I am currently a PhD student at the MLIA team, at the LIP6 lab, in Sorbone Université, Paris. I am mainly working on visual reasoning tasks, such as Visual Question Answering. My research is funded by the VISADEEP ANR chair

I was previously intern at the Cognitive Machine Learning team at Ecole Normale Supérieure, in Paris, as a machine learning research intern.

I’m very interested in machine learning and deep learning, more specifically for robotics, computer vision, and speech processing, and its application for AI research.


PhD Student, Sorbonne Université, Paris I am currently a PhD candidate at Sorbonne Université. My PhD subject is “Deep Learning for Visual Reasoning”.

Master degree, Computer Science, Georgia Institute of Technology I graduated from Georgia Tech, with a specialization in machine learning and interactive intelligence.

Engineering student, Ecole Centrale Paris, France I studied Engineering and Computer Science at Ecole Centrale Paris.

Internships and projects

Research Internship, CoML, Spring 2018
I interned for 6 months at the CoML team, a research lab in École Normale Supérieure in Paris. I worked on unsupervised machine learning for words and phoneme discovery in speech data.

I contributed to the package ABNet3, a siamese neural network for speech embedding. The package is available on github:

Study of the Variational Auto Encoder for speech subword modeling
For a class project, at Georgia Tech, I studied a variational Auto Encoder architecture for the Zerospeech Challenge, Track 1: unsupervised subword modeling. You can find my work here:

Deep learning for optical flow estimation, Fall 2017
As a graduate student at Georgia Tech, I worked on a project to estimate optical flows in a natural environment dataset. I used the FlowNet2 architecture. You can find my code on github

Software engineering internship, Datadog, Fall 2016
I worked in the data engineering team, managing data pipelines with Spark and Hadoop.



  1. Fishr: Invariant gradient variances for out-of-distribution generalization Alexandre Rame, Corentin Dancette, and Matthieu Cord ICML 2022 [Abs] [BibTex] [PDF] [Code]


  1. Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering Corentin Dancette, Remi Cadene, Damien Teney, and Matthieu Cord ICCV 2021 [Abs] [BibTex] [PDF] [Code]
  2. Overcoming Statistical Shortcuts for Open-ended Visual Counting Corentin Dancette, Remi Cadene, Xinlei Chen, and Matthieu Cord Visual Question Answering workshop, CVPR 2021 [Abs] [BibTex] [arXiv] [PDF] [Code]



    1. RUBi: Reducing Unimodal Biases for Visual Question Answering Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, and others In NeurIPS 2019 [Abs] [BibTex] [arXiv] [PDF] [Code]


    1. A K-nearest neighbours approach to unsupervised spoken term discovery Alexis Thual, Corentin Dancette, Julien Karadayi, Juan Benjumea, and Emmanuel Dupoux In 2018 IEEE Spoken Language Technology Workshop (SLT) 2018 [Abs] [BibTex]
    2. Sampling Strategies in Siamese Networks for Unsupervised Speech Representation Learning Rachid Riad, Corentin Dancette, Julien Karadayi, Neil Zeghidour, Thomas Schatz, and Emmanuel Dupoux In Proc. Interspeech 2018 [Abs] [BibTex]


    Apr 2022 Detecting and reducing data biases
    Sicara, Paris
    Apr 2022 Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
    ICCV 2021
    Nov 2020 Detecting and reducing data biases, Paris
    Feb 2020 RUBi : Reducing Unimodal Biases in Visual Question Answering
    ENS Ulm, LSCP, CoML Team, Paris
    Dec 2019 RUBi : Reducing Unimodal Biases in Visual Question Answering
    NeurIPS 2019